A robot capable of manipulating its environment is more useful than one that can only perceive things. Such robots will be able to carry out many of our daily chores thereby relieving humans for more creative pursuits. For this to happen, the robots are required to have human-like manipulation and grasping abilities. While the manipulation abilities of robots have matured over the years, grasping still remains a difficult problem which has attracted a lot of attention in the recent past, thanks to the rapid advancement in computer vision and image processing techniques. A number of methods exist in literature that attempt to solve this grasping problem. Some of them use visual features in 2D images to localize graspable regions while others use range data for this purpose, the later becoming more popular owing to the availability of low cost RGBD sensors. Recently, the deep learning based methods are becoming increasingly popular for detecting graspable regions. Most of the existing methods in this field can be broadly classified into two categories. First category, wherein method(s) relies on the availability of accurate geometric information about the object (or a CAD model) making them impractical in several real-world use cases. The second category of methods focus on computing the grasp pose as well as the grasping affordances directly from a RGBD point cloud. Such methods as described above make use of local geometric features to identify graspable regions without knowing the object identity or its accurate 3D geometry. The problem yet remains and is challenging due to several factors like partial occlusion, poor illumination, change of shape and size of deformable objects, scaling as well as restricted field of view for the rack bins.